Catalog
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NeuralNetwork Abstract and Links for each page
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Basic Algorithms
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Neural Network parameters
About neural network paramaters, weights and bias and matrix manipulation.
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Auto Encoder
This tutorial introduces the auto encoder technique.
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Introduction to the Loss Function
Introduction to two basic loss funcions, and related activation functions
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Intro to Activation Function
We will introduce the basic activation function and its features.
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Inside the Activation Function
Understanding the purpose and inside of activation function
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Activation Function Types
In this tutorial, we will show the types of activation functions that are in ReNom.
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Stochastic Gradient Descent(SGD) Settings
Effects of Stochastic gradient descent settings
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Adagrad Optimization
Introduction to Adagrad Optimization
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Adam Optimization
Introduction to Adam Optimization
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Batch Normalization
How to use batch normalization with ReNom
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Dropout
Dropout using fully connected neural network model to mnist.
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Weight Decay
How to use weight decay with ReNom using fully connected neural network model to mnist.
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Convolutional Neural Network(CNN)
In this chapter, we will introcduce the convolutional neural network(CNN) used in mainly computer vision tasks.
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Precision and Recall
Precision and recall evaluation, using the “Adult Data Set.”
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Correlation Coefficient and Coefficient of Determination
This is a simple introduction to correlation coefficients and coefficients of determination, using the Boston housing price dataset.
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Preprocessing
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Time Series Interpolation
Time series interpolation
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Completion to numerical data and categorical data
Completion to numerical data and categorical data, using pseudo missing data
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Merge the Categorical Data and the Mumeric Data
How to merge the categorical data and the numeric data using adult dataset.
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Normalization and Standardization
Learning come to slow case, and prevent to use normalization and standardization.
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Onehot Conversion for Categorical Data
The good point of onehot vector to learn
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Preprocessing for Embedding Layers
We will explain preprocessing needed to use embedding layer on Renom.
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Resize Preprocessing for Image Classification
Resize preprocessing for image classification using caltech dataset.
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Zoom Preprocessing for Image Classification
Zoom preprocessing for image classification using caltech dataset
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Flip Preprocessing for Image Classification
Flip preprocessing for image classification using caltech dataset
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Rotate Preprocessing for Image Classification
Rotate preprocessing for image classification using caltech dataset
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Shift Preprocessing for Image Classification
Shift preprocessing for image classification using caltech dataset
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ColorJitter Preprocessing for Image Classification
ColorJitter preprocessing for image classification using caltech dataset
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Crop Preprocessing for Image Classification
Crop preprocessing for image classification using caltech dataset
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Data Visualization
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Time Series Data
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Image Data
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Digit Image Classification
Digit image classification problem using fully connected neural network model to mnist.
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Image classifier
An introduction of Convolutional neural network and how to use GPU.
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image Binary Classification
Image binary classification problem using Convolution neural network model to Caltech 101
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Neural Style Transfer
Implementation of “Image Style Transfer Using Convolutional Neural Networks.”
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Using Pre-trained Caffe model in ReNom
Loading the weights of pre-trained Caffe model into ReNom model.
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Object detection using YOLO
An example of object detection
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Fully Convolutional Networks for Semantic Segmentation
This chapter introduces Fully Convolutional Network for Semantic Segmentation.
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Conversion of a movie to a series of images
Conversion of a movie which is a kind of mp4 or avi to a series of images
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Clustering
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Regression
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Generative Model
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Embedding
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ReNom DL
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ReNom Basic Calculation using Auto Differentiation
This is an introduction of ReNom basic calculation using auto differentiation.
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Sequential Model
An introduction to how to build a sequential model
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Functional Model
An introduction of how to build functional model.
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Concatenate Layer Output
Concatenate layer output using fully connected neural network model to mnist.
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Trainer
An introduction of trainer function. Trainer function simplify training loop.
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Saving and Loading Models
An introduction of saving and loading learned models.
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Inference mode
Inference mode used for Dropout and Batch Normalization.
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Hyperparameter Search
Hyperparameter search example, using the MNIST data
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ReNom IMG
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ReNom TAG
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ReNom DP
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TDA Basic
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How to use ReNom TDA
An introduction of how to use ReNom TDA.
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How to save Topology
An introduction of how to save topology.
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Search Data
An introduction of searching topology data.
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How to show Point Cloud
An introduction of show point cloud data.
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How to clustering Point Cloud
An introduction of clustering point cloud data.
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How to use 2D data
An introduction of using aleady dimension reduced data.
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Convert Excel to CSV
An introduction of converting Excel data to csv file.
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MNIST Dataset Mapping
An introduction of MNIST dataset mapping using ReNom TDA.
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How to use ReNom TDA GUI
An introduction of how to use ReNom TDA GUI.
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TDA Case Study
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Database Interface